Biology subjects

Sarah A Gagliano

Publications and source records attributed to Sarah A Gagliano.

3 recordsLinked to original sources

bioRxiv2016

Genetic variability in both the adaptive and innate immune systems contribute to Alzheimer’s and Parkinson’s disease risk

Neurodegenerative disorders are devastating diseases with a worldwide health-care burden. Studies have demonstrated enrichment of disease-associated genetic variants with functional genomic annotations. Determining associated cell-types is important to understand pathogenicity.\n\nWe obtained GWAS summary statistics from Parkinsons disease (PD), Alzheimers disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and frontotemporal dementia (FTD). We applied stratified LD score regression to determine if functional categories are enriched for heritability.\n\nThere was little enrichment of brain annotations, but annotations from both the innate and adaptive immune systems were enriched for MS (as expected), AD, and PD, in decreasing order of statistical significance.

Genomics↗

bioRxiv2014

Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants

Although technology has triumphed in facilitating routine genome re-sequencing, new challenges have been created for the data analyst. Genome scale surveys of human disease variation generate volumes of data that far exceed capabilities for laboratory characterization, and importantly also create a substantial burden of type I error. By incorporating a variety of functional annotations as predictors, such as regulatory and protein coding elements, statistical learning has been widely investigated as a mechanism for the prioritization of genetic variants that are more likely to be associated with complex disease. These methods offer a hope of identification of sufficiently large numbers of truly associated variants, to make cost-effective the large-scale functional characterization necessary to progress genome scale experiments. We compared the results from three published prioritization procedures which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding of the functional annotations. In this paper we also explore different combinations of algorithm and annotation set. We train the models in 60% of the data and reserve the remainder for testing the accuracy. As an application, we tested which methodology performed the best for prioritizing sub-genome-wide-significant variants (5x10-8<p<1x10-6) using data from the first and second rounds of a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71). However, predictive accuracy results obtained from the test set do not always reflect results obtained from the application to the schizophrenia meta-analysis. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved towards the step change in the risk variant prediction required to address the impending bottleneck of the new generation of genome re-sequencing studies.

Bioinformatics↗

bioRxiv2013

A Bayesian Method to Incorporate Hundreds of Functional Characteristics with Association Evidence to Improve Variant Prioritization

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals (\"hits\") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.\n\nAuthor SummaryLarge-scale genetic studies have had success identifying genes that play a role in complex traits. Advanced statistical procedures suggest that there are still genetic variants to be discovered, but these variants are difficult to detect. Incorporating biological information that affect the amount of protein or other product produced can be used to prioritise the genetic variants in order to identify which are likely to be causal. The method proposed here uses such biological characteristics to predict which genetic variants are most likely to be causal for complex traits.

Bioinformatics↗